Mortality rate distribution within sex and race stratification in CA
# create distribution graph graph to find association between gender and death rate
p1 <- ggplot(CA_gender, aes(Data_Value, fill = Stratification1))+
geom_density(alpha = 0.5) +
scale_fill_brewer(palette = "Set3") +
labs(
x = "death rate per 100,000 population",
y = "Density",
title = "Distribution of death rate by gender in CA")
p1 <- ggplotly(p1)
# create distribution graph to find association between race and death rate
p2 <- ggplot(CA_race, aes(Data_Value, fill = Stratification2))+
geom_density(alpha = 0.5) +
scale_fill_brewer(palette = "Set3") +
labs(
x = "death rate per 100,000 population",
y = "Density",
title = "Distribution of death rate by race in CA")
p2 <- ggplotly(p2)
Mortality rate pattern under sex stratification in CA during 2014
Mortality rate pattern under race stratification in CA during 2014
Mortality rate Gap Between Male group and Female group in CA
fig_gendergap <- plot_ly(gender_joint, x = ~value_male, y = ~value_female, text = ~LocationDesc, type = 'scatter', mode = 'markers',size = ~Gap, color = ~LocationDesc, colors = 'Paired',
sizes = c(5, 45),
marker = list(opacity = 0.5, sizemode = 'diameter'))
fig_gendergap <- fig_gendergap %>%
layout(title = 'Gender Gap on heart disease death rate among CA county',
xaxis = list(title = 'Mortality rate/100,000 population (male)', showgrid = FALSE),
yaxis = list(title = 'Mortality rate/100,000 population (female)', showgrid = FALSE))
fig_gendergap
Mortality Rate Pattern in each county in CA (gender stratification)
## Pattern map for male group
fig_male <- plot_ly( text=~paste(paste("County: ", CA_gender1$LocationDesc),
paste("Death rate/100,000:", CA_gender1$value_male),
sep="<br>"),hoverinfo="text")
fig_male <- fig_male %>% add_trace(
type="choroplethmapbox",
geojson = counties,
locations = CA_gender1$fips,
z = CA_gender1$value_male,
colorscale="Viridis",
zmin = 150,
zmax = 500,
marker=list(line=list(
width=0),
opacity=0.5))%>%
layout(
mapbox=list(
style="carto-positron",
zoom =4,
center=list(lon= -119.42, lat=36.78)))
fig_female <- plot_ly( text=~paste(paste("County: ", CA_gender1$LocationDesc),
paste("Death rate/100,000:", CA_gender1$value_female),
sep="<br>"),hoverinfo="text")
fig_female <- fig_female %>% add_trace(
type="choroplethmapbox",
geojson = counties,
locations = CA_gender1$fips,
z = CA_gender1$value_female,
colorscale="Viridis",
zmin = 150,
zmax = 500,
marker=list(line=list(
width=0),
opacity=0.5))%>%
layout(
mapbox=list(
style="carto-positron",
zoom =4,
center=list(lon= -119.42, lat=36.78)))
Male group mortality rate in CA during 2014
Female group mortality rate in CA during 2014
Mortality Rate Pattern in Each County in CA During 2014(Race Stratification)
## Pattern map for White group
fig_white <- plot_ly(
text=~paste(paste("County: ", CA_race1$LocationDesc),
paste("Death rate/100,000:", CA_race1$value_white),
sep="<br>"),hoverinfo="text")
fig_white <- fig_white %>%
add_trace(
type="choroplethmapbox",
geojson = counties,
locations = CA_race1$fips,
z = CA_race1$value_white,
colorscale="Viridis",
zmin = 150,
zmax = 500,
marker=list(line=list(
width=0),
opacity=0.5)) %>%
layout(
mapbox=list(
style="carto-positron",
zoom =4,
center=list(lon= -119.42, lat=36.78)))
# Pattern map for Hispanic group
fig_hispanic <- plot_ly(
text=~paste(paste("County: ", CA_race1$LocationDesc),
paste("Death rate/100,000:", CA_race1$value_hispanic),
sep="<br>"),hoverinfo="text")
fig_hispanic <- fig_hispanic %>% add_trace(
type="choroplethmapbox",
geojson = counties,
locations = CA_race1$fips,
z = CA_race1$value_hispanic,
colorscale="Viridis",
zmin = 150,
zmax = 500,
marker=list(line=list(
width=0),
opacity=0.5))%>%
layout(
mapbox=list(
style="carto-positron",
zoom =4,
center=list(lon= -119.42, lat=36.78)))
# Pattern map for Black group
fig_black <- plot_ly(text=~paste(paste("County: ", CA_race1$LocationDesc),
paste("Death rate/100,000:", CA_race1$value_black),
sep="<br>"),hoverinfo="text")
fig_black <- fig_black %>% add_trace(
type="choroplethmapbox",
geojson = counties,
locations = CA_race1$fips,
z = CA_race1$value_black,
colorscale="Viridis",
zmin = 150,
zmax = 500,
marker=list(line=list(
width=0),
opacity=0.5))%>%
layout(
mapbox=list(
style="carto-positron",
zoom =4,
center=list(lon= -119.42, lat=36.78)))
# Pattern map for Asian and Pacific Islander group
fig_asian_pacific <- plot_ly(text=~paste(paste("County: ", CA_race1$LocationDesc),
paste("Death rate/100,000:", CA_race1$value_asian_pacific),
sep="<br>"),hoverinfo="text")
fig_asian_pacific <- fig_asian_pacific %>% add_trace(
type="choroplethmapbox",
geojson = counties,
locations = CA_race1$fips,
z = CA_race1$value_asian_pacific,
colorscale="Viridis",
zmin = 150,
zmax = 500,
marker=list(line=list(
width=0),
opacity=0.5))%>%
layout(
mapbox=list(
style="carto-positron",
zoom =4,
center=list(lon= -119.42, lat=36.78)))
fig_indian_alaskan <- plot_ly(text=~paste(paste("County: ", CA_race1$LocationDesc),
paste("Death rate/100,000:", CA_race1$value_indian_alaskan),
sep="<br>"),hoverinfo="text")
fig_indian_alaskan <- fig_indian_alaskan %>% add_trace(
type="choroplethmapbox",
geojson = counties,
locations = CA_race1$fips,
z = CA_race1$value_indian_alaskan,
colorscale="Viridis",
zmin = 150,
zmax = 500,
marker=list(line=list(
width=0),
opacity=0.5))%>%
layout(
mapbox=list(
style="carto-positron",
zoom =4,
center=list(lon= -119.42, lat=36.78)))
Asian and Pacific Islander
American Indian and Alaskan Native